Out of home digital ad server

ABSTRACT

There is provided a method for an out of home advertising campaign, the method comprising: supplying creative for the campaign; determining criteria for the campaign, the criteria comprising targeting a demographic; selecting one or more boards for display of the creative, the selecting based on static data, projected data, and optionally real-time data; and displaying the creative on the one or more boards. There is also provided an out of home digital ad server comprising: at least one digital board; a digital feed provider to provide each board with creative to be displayed; a computer processor for analysing data to optimize board selection based on a demographic; and a communication network to direct creative from the ad serving processor to the at least one digital board based on the selection of creative by the computer processor.

FIELD OF THE INVENTION

The present invention pertains to a system and method for distributingdigital advertisements to a board in order to maximize reach for adesired target audience or demographic. More particularly, the presentinvention pertains to distributing digital creative to a network of outof home or public digital boards.

BACKGROUND

Ad serving is the process of taking an advertisement and distributing itintelligently in an attempt to maximize customer reach. Ad serving is away of connecting advertisers, who want their ads to be seen, to peoplewho are most likely to be interested in the product being advertised. Adserving is commonly done on online websites and mobile devices.Advertisers do not buy a spot on a particular website; instead they payfor impressions and select criteria such as a target demographic. The adserver tries to dispatch the ad intelligently to the people that aremost likely to be interested in its offer based on previously collectedinformation. In an at-home model, search engines can take advantage ofuser behaviour to target individual users based on internet searches.For example, if a customer does a search for cruises in the Caribbean ontheir personal networked device, this search can be recorded so thatadvertising for cruises in the Caribbean can be directly sent to theuser in online advertising spaces.

There are many tools known in the art for undertaking statisticalanalysis and data mining and analyzing information on data. UnitedStates Patent Application Publication No. 2013/0073388 to Heathdescribes a system for mobile and internet advertising based oncollecting browser history, user preferences, social networking info,and linking it all together to present appropriate ads for that user.Heath is directed at advertising to single users of a networked device,and describes showing customized ads based on a user's browsing historyand the location of the user. For example the advertiser could send theuser a customized list of interesting products as the user enters astore, and a coupon code to entice the user to come back to the store.

However, such a concept is harder to apply in the out-of-homeadvertising world. The website ad serving model relies on building aprofile for each individual user through various means (web history,social network profiles, ads clicked in the past, etc.), and buildingsuch an individual profile is simply not possible in out-of-homeadvertising. As it is, ad serving in this industry is very limited, andusually does little more than push ads based on geographical location.

In an out-of-home advertising campaign it can be challenging todetermine where to place certain advertisements in order to reach thetarget audience. Although digital board location can be scored based onnumber of impressions, it is far more important for success in anadvertising campaign to maximize the reach to potential customers,rather than simply to maximize the number of impressions. In oneexample, an advertisement for a women's deodorant on a digital board ina men's locker room may generate many impressions; however it isunlikely to result in increased revenue for the deodorant, unless, ofcourse, the advertisement was aimed at the men.

Digital signage is now prevalent in public places and is being widelyused by advertisers. Locations for placement of digital signs areselected by out-of-home decision engines that optimally choose boardlocations based on campaign objectives to maximize the number of thepeople who might view them. Most digital out of home advertising is soldthrough the concept of a loop: a digital billboard (“board”) may haveits uptime split between 6 spots of 10 seconds each, creating a 60second loop. Advertisers purchase spots in a loop, during which theircreative will play.

At its core, advertisers select various settings, such as demographics,time of the day and keywords that will assist in selecting the idealboards for displaying a particular advertising campaign. They set abudget, which is consumed as the ads are displayed. In one example,United States Patent Application Publication No. 2008/0215290 to Zwebendescribes a method of determining a location based advertising campaignby scoring available physical spots in an advertising supply anddetermining which from among the available spots in physical locationsto include in the campaign based on an advertising budget and thescoring of the available spots, with scoring based on the number ofimpressions. Zweben goes through the entire campaign creation process,from receiving customer orders to designing creative and reviewing themfor appropriateness to generating a reports. The board selection processworks by generating all boards that match the advertiser's request,ordering them by relevance, and picking the top ones until theadvertiser's budget is spent. Once each board is scored and selected bythe advertiser, the display method remains unchangeable, meaning thatthe advertiser places their creative at a particular board for aparticular period of time, and the placement and/or schedule is notsubject to change based on external characteristics.

Each board vendor also wants to present their boards as highly-valuableand perfect for each advertiser's individual needs, however assessmentof the value of a particular board for placement of a particularadvertisement can be challenging. There remains a need fornon-subjective assessment of the selection of particular out of homedigital boards for serving advertisements.

This background information is provided for the purpose of making knowninformation believed by the applicant to be of possible relevance to thepresent invention. No admission is necessarily intended, nor should beconstrued, that any of the preceding information constitutes prior artagainst the present invention.

SUMMARY OF THE INVENTION

An object of the present invention is to provide an out of home adserver for selecting and displaying creative based on the demographic ofthe desired target audience.

In accordance with one aspect, there is provided a method for an out ofhome advertising campaign, the method comprising: supplying creative forthe campaign; determining criteria for the campaign, the criteriacomprising targeting a demographic; selecting one or more boards fordisplay of the creative, the selecting based on static data, projecteddata, and optionally real-time data; and displaying the creative on theone or more boards.

In accordance with one embodiment, the selecting is based on real-timedata, and wherein the real-time data is continuously updated.

In accordance with another embodiment, the selecting of the one or moreboards is updated in real-time such that the selection of which boardsand creative is displayed are changed during the campaign.

In accordance with another embodiment, the creative comprises images,video, web pages, dynamic elements, countdown timers, stock tickers, andany combination thereof.

In accordance with another embodiment, the method further comprisesgenerating an ongoing report so that an advertiser can adjust thecampaign, the creative, and/or a budget during the campaign.

In accordance with another aspect, there is provided an out of homedigital ad server comprising: at least one digital board; an ad servingprocessor to provide each board with creative to be displayed; acomputer processor for analysing static data, projected data andoptionally real-time data to optimize board selection based on ademographic and determine which campaign each board should be playing,the computer processor selecting which creative is displayed on the atleast one digital board; and a communication network to direct creativefrom the ad serving processor to the at least one digital board based onthe selection of creative by the computer processor.

In accordance with one embodiment, the ad server further comprises acommunication link to a data network to obtain real-time data from thedata network.

In accordance with another embodiment, the ad server further comprises aplurality of digital boards.

In accordance with another embodiment, the at least one digital boardfurther comprises a memory for recording playback-related statistics onthe creative played on the at least one digital board.

In accordance with another embodiment, the at least one digital boardfurther comprises a memory for storing the creative content, which isthen downloaded and stored locally in a local database or file system.

In accordance with another aspect, there is provided a method forexecuting an out of home advertising campaign, the method comprising:determining static demographic data for at least one digital board;determining projected demographic data for the at least one digitalboard; determining real-time demographic data for the at least onedigital board combining the static demographic data, projecteddemographic data, and real-time demographic data to select and displayadvertisements to be displayed on the at least one board.

In accordance with one embodiment, the real-time data based on socialmedia trending or human movement tracking.

BRIEF DESCRIPTION OF THE FIGURES

For a better understanding of the present invention, as well as otheraspects and further features thereof, reference is made to the followingdescription which is to be used in conjunction with the accompanyingdrawings, where:

FIG. 1 depicts an exemplary process flowchart of the described adserver;

FIG. 2 depicts an exemplary process flowchart of a campaign purchaseprocess;

FIG. 3 depicts an exemplary process flowchart of a board classificationprocess;

FIG. 4 depicts an exemplary process flowchart of a board selectionprocess; and

FIG. 5 depicts an exemplary process flowchart for optimizing the boardselection.

DETAILED DESCRIPTION OF THE INVENTION Definitions

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs.

As used in the specification and claims, the singular forms “a”, “an”and “the” include plural references unless the context clearly dictatesotherwise.

The term “comprising” as used herein will be understood to mean that thelist following is non-exhaustive and may or may not include any otheradditional suitable items, for example one or more further feature(s)and/or component(s) as appropriate.

The term “creative” as used herein is a term of art used to refer to anad or set of ads for an advertising campaign. Non-limiting examples ofadvertising creative include still images, video, web pages, movingimages, dynamic elements such as countdown timers or stock tickers, andany combination thereof.

The term “board” as used herein refers to any digital public signage ordigital billboard mounted in a public space. Public spaces includeoutdoor public spaces, which include but are not limited to roadways,walkways, arenas and parks. Boards can also be located in indoor publicspaces in buildings, non-limiting examples of these include clubs,restaurants, elevators, shopping malls, theatres, office buildings,sports centres, recreation facilities, health clubs and retailestablishments.

The term “demographic” as used herein refers to a group of people thathave a particular characteristic in common. Non-limiting characteristicson which a demographic can be defined are age (such as being within aparticular age range), sex, race, sexual preference, socio-economicstatus, geographical location, or religion. Also included in this termare interest-based groups of individuals that share a common interest,either by public affiliation, or by interest derived from social media.Non-limiting examples of public affiliations that can be considered asdemographic groups are club or organization members, employees of aparticular company or organization, fans of a sports franchise, orstudents at a college or school. Interests can also be gleaned fromsocial media based on a person's likes of, for example, particularhobbies, music groups, sports, or brands.

As used herein, the term “Big Data” refers to a collection of data setsthat is very large. Such datasets have become increasingly available foranalysis and trends can be gleaned by correlating selected data. Bigdata sets are routinely collected from the behaviour of individualsonline as well as offline, and from human movement captured bypositioning data. Big Data can be mined to find useable trends andpatterns for ad serving.

As used herein, the term “out of home” refers to any indoor or outdoorpublic space where the digital board can be put on display.

Described herein is a system and method for distributing advertisementcreative to a network of digital boards in order to maximize reach for adesired target audience. The presently described system employs data toeffectively reach the desired target audience for the advertisement. Thepresent system and method uses past, present and future information tolearn and adjust itself in order to maximize reach for a desired targetaudience. In this way, static demographic data as well as broader trendscan be used to select ads for display on a particular board that arerelevant to the people currently in the area near the board.

The present method and system use data analytics to improve the reachand placement of out-of-home advertising creative. By tapping into awide variety of data sources, boards can be intelligently classified inorder to send the right ad to the right location at the right time. Inaddition, the system is enabled to constantly adjust itself based onreal-time data, re-classifying its boards in real-time to accommodateemerging trends. Future projections are also made, allowing the systemto plan ahead of time which content should be displayed at whichlocation. A combination of static, projected and real-time data is thuscombined to optimize display of advertisement to maximize impact andimpressions of appropriate viewers.

When creating a campaign, advertisers must provide criteria used tocontrol how, where and when the ads will play as well as evaluate thesuccess of the campaign and how they will be charged for it. Possiblecriteria can include but are not limited to: target demographics by age;sex; ethnicity; geographical location; interests; number of potentialcustomers who saw the ad; and time of day. These criteria are then usedto determine which boards should play that campaign's content. Boardscan be classified based on properties that indicate which campaigncriteria they are able to fill.

The present system and method extend and transform a conventionaldigital signage “software player” to an ad server that mimics thebehaviour of online ad servers on the Internet. Static and real-timedata can be tapped into to determine what's trending. Social mediawebsites, music websites, application programming interfaces (APIs),cellular carrier data insights, and other databases can also tap intoout-of-home ratings, and can be applied as real-time data.Cross-referencing data from these sources to determine what's trendingby demographic enables the gathering of anonymous information onvehicular and pedestrian observers passing by digital billboards toenable behavioural and contextual targeting. In this way, digitalbillboard companies can sell budget and demographic audiences instead ofsimply placement in specific locations and/or at specific times.

For smaller advertisers that cannot necessarily afford to book 10 boardsfor 4 weeks, or a pricey advertising loop desired by the board spacevendor, selling via ad serving offers an opportunity to set a smallbudget and get pointed exposure to a particular target market. The smalladvertiser may not have done the market research to know that the peopleinterested in their product are male 18-30 with a degree in computerscience. However, social media that provides information not only aboutdemographics such as age, sex, education level, can also provideinformation about interests, linking the small advertiser to aparticular demographic which can be targeted using Big Data.

Data Types

The present method and system employ three main types of data: staticdata, projected data and real-time data. Real-time analysis of data isused to determine which demographic each board is most exposed to at anyparticular time. Even if no particular trend can be identified throughreal-time data analysis, projected data and static data can be combinedto classify a board.

Static data is data that does not change for a certain period of time.It is assumed to be valid until a new set of data comes to replace it.Non-limiting examples of static data include traffic and populationsurveys, market studies, consumer spending trends and more. This datacan have a time component, indicating that it is only valid for certaintimes of the day or at certain dates throughout the year. Static dataserves as a baseline so that every board can be given basic informationabout which demographics it can serve. If nothing out of the ordinary ishappening around the vicinity of the board, this data will be used as-isfor campaign targeting purposes. Non-limiting examples of informationthat can be extracted from static data are:

-   -   Which demographics live there    -   Which demographics work there    -   Which demographics commute regularly through this area    -   How many people can potentially see the board each day    -   What are the most commonly purchased products in this area    -   How does this information fluctuate over time during a single        day, and on weekdays compared to weekends    -   What are the interests of the demographics in the area

Projected data is data about specific events that will happen in thefuture which can bring in a unique target audience from the one usuallyfrequented in the vicinity of the board. It begins and ends at a knownmoment in time. Non-limiting examples of specific events that can befactored in to projected data include sport events, music concerts,holiday parades, political rallies, etc. These may be one-time events orrepeating events. By looking at this type of data, board information canbe adjusted to better reflect the reality of the people near that boardat that point in time. Demographics can shift, new interests may beserved, the number of people near that board can be predicted to behigher or lower than usual, etc. This data allows the system to adjust,in advance, to better reflect what the reality at that moment should be.Examples of information that can be extracted from projected datainclude but are not limited to:

-   -   What kind of event happens here, and what kind of demographics        do they bring?    -   How different are the regular demographics and those that come        only for special events?    -   Is there a regularity to the events here?    -   Is this an emerging area, meaning that the temporary trends        could soon become permanent?

Real-time data is data that must be analyzed as it arrives. It isusually unpredictable and short-lived. Examples include but are notlimited to breaking news coverage, social network feeds, public reactionto events, etc. This data usually has geographical informationassociated with it. By analyzing this data, its content, its author, andits location, more adjustments can be done to better pinpoint theaudience the board can serve. Real-time data is different from the otherdata sources in that there are two dimensions to its use. First, themessage is important, because it can indicate trending interests inspecific areas. But even if the message is irrelevant, the informationabout that author's message can be a rich source of information. Thedemographics of the people in the vicinity of the board can be obtainedby tapping into the personal profiles of the people in the vicinity ofthe board, and ads can be selected and served tailored to thesedemographics. Some non-limiting examples of information that can beextracted from real-time data are:

-   -   Profile of the user who made a social networking post while        being close to a board    -   Pictures taken close to a board    -   Hashtags and other such keywords in messages    -   Groups, events, activities and other such metadata that can be        linked in a message    -   Nearby location, such as a commerce, that a user has identified        as being currently located (“checked-in”)    -   Current traffic condition on roads near the board

Real-time data can be obtained through social media sites such as, forexample, Facebook® and Twitter®. In one example, if a particular productis trending on social media, increasing the display of creative directedto that product at the same time can result in a greater overallimpression to viewers than if the same creative is displayed at a timewhere there isn't trending support on social media. For example, if asports star tweets a favorite brand, board display of creative for thesame brand can be intensified for a certain period of time to solidifythe viewer impression.

Application of Data Types to Ad Serving

Advertisers set a budget and target demographics through variouscriteria such as, for example, explicit demographic choice, producttype, keywords, or time of day. The network collects trends through dataanalytics, also referred to as “Big Data”, in order to categorize theboards, adjusting in real-time to better reflect the audience currentlynear each board. In this way, the creative loop can be automaticallyadjusted based on social trending rather than merely on demographic andpopulation data (static data) obtained from databases. The creativeplacement planning process occurs and is updated constantly inreal-time, creative is shifted around to accommodate trending andspecial events, and the creative loop on boards is changed while thecampaign is running. Continually tweaking of the campaign by tappinginto social trending and considering historical data and current trendsprovides an analysis of short-term impression projections, which areused to catalogue and categorize the boards in real-time.

As shown in FIG. 1, an advertiser 2 who wishes to launch an advertisingcampaign can place their creative based on demographic of users ratherthan simply on board location or capability. Vendors of space on publicboards expose their inventory 4, including details of the boards such aslocation and size. Data sources, such as from Big Data 6, contributeanalytical data to a real-time demographics categorizing process 12, aswell as to a real-time board categorizing process 8. The boards are thenre-categorized based not only on static and projected data, but also onreal time data, and a determination is made on board characterization byserved demographics 10. Concurrently, demographics trends are followed14, for example by product type or keywords, and can also be tracked byinterest, human movement, traffic movement or spontaneous social mediatrending. In this way, the advertising campaign is shifted to selectionof ads and locations based on demographic of viewers 16 in the vicinityof the board, and the campaign is targeted to a particular demographic18 that is updated in real-time. A board selection optimization engine20 then selects the ideal boards on which to play the advertisement, andthe boards with creative to display 22 are provided with creative onvideo players 24 a, 24 b and 24 c.

Advertisers can also bid for spots, and can be charged based on howrelevant the boards are to their needs, with price adjusted based onfluctuating parameters such as demand for the selected boards. The adserving system connects these together, sending creative to the boardsthat are currently able to best meet the advertiser's criteria. Realtime trending on social networks is thus factored into board selectionand creative rotation, and the system rotates the creative to maximizedemographic fulfillment. The creative can thus be shifted around todifferent boards at different times while the campaign is running tooptimally target the desired demographic. Play logs and trends can alsobe collected to construct reports presented to advertisers in order toprove the relevance of the boards and display times that have beenselected for them.

Although some digital billboards are individually run, there are a fewlarge digital billboard companies which control and/or own manythousands of out-of-home public boards. Not only will each of theseboards have different classifications based on static data, but theywill have different time-dependent classifications based on projectedand real-time data. The present system and method can be usedinternally, by the company itself, to optimize ad placement of eachcreative based on the real-time classification of each board. Largedigital billboard operators may also be interested in opting for asingle-company ad server that they can have more control over. Inaddition, cross-company ad serving platforms can be envisaged whichselect digital boards with particular real-time classifications tooptimize placement of creative. For example, signage around transitstations or restaurants around an arena can be owned/operated bydifferent companies, however it may be useful for a particularadvertiser to purchase board time for a time range around a concert toadvertise a ware or service targeted to the concert-goers.

One embodiment of the present system is comprised of a digital signagesoftware player that has ad serving capabilities. Digital billboardoperators can then, instead of selling spots in loops and day parts,sell an audience or demographic to an advertiser that can be retargetedwhile the campaign is running, in a similar way as it accomplished inthe online media world. Large digital billboard companies can also tradein playlist-based players for ad serving players.

Board vendors can also connect their boards with the present ad serverthrough a communication network. The boards can register themselves,providing data about them such as their size, location and orientation.Operators can manage their inventory through software such as a webapplication. Each board is connected to a nearby digital feed provider,such as a computer or embedded system, also commonly referred to as avideo player. That video player connects with the ad server and requestscontent, which is then downloaded and stored locally in a local databaseor file system. Content can then be played on the connected screens orboards. The video player periodically reconnects with the server toupdate itself and get an updated list of content to play, which may havechanged due to new advertisement contracts or evolving data trends. Aspart of this communication, the video player also submits a report ofthe content it played so that the ad server can keep track of it forvarious purposes such as consuming the advertiser's budget andassembling an aggregated report based on data from various videoplayers. Optional screen captures, such as from a camera facing theboard, can be sent and collected by the ad server for proof ofperformance reporting purposes.

Advertisers can purchase screen time through software such as at aweb-accessible store, where they can specify various settings such asdesired demographics, type of product being advertised, keywords, etc.,that will allow the system to target on which boards their ad shouldrun. They set a budget, and the system produces an estimate of theboards and impressions they might obtain. The software can also allowthe advertiser to upload and manage their own creative. As theircampaign runs, the advertiser can get feedback in the form of anaggregated report, and can alter or extend their campaign as needed.

FIG. 2 illustrates the method of an advertiser purchasing a campaignthrough the system. The advertiser 102 initiates the campaign byindicating a desire to place creative in an out-of-home advertisingsystem. The advertiser can optionally use a service, such as a website,to identify themselves and initiate the campaign creation process. Theadvertiser supplies the creative 104 a, which is the content that willplay on the boards. Examples of creative include but are not limited toimages, video, web pages, dynamic elements such as countdown timers orstock tickers and any combination thereof. The advertiser may alsosupply one or multiple of such creative.

The criteria 104 b represents what the advertiser wants to accomplishwith the particular advertising campaign. Examples includes targetdemographics, a total impression count, geographical locations ormarkets that should be served, and possible interests and keywords suchas, for example “near a transit station”, “near a school”, “near arecreation facility”, etc. These are used to determine the appropriatelocation to display the campaign and the end evaluation of the successof the campaign. The budget 104 c set by the advertiser that willdetermine how long or how broad his campaign will be. Every time an adplays, budget is consumed based on various factors. The campaign is overonce the budget is fully spent.

After deciding on the details of the campaign, the advertiser ispresented with an estimate of how the campaign will take place by way ofa preview of the campaign 106. Information presented as part of thecampaign preview can include a list of possible boards, how many timesthe ad can be expected to play, which times of the day the creative willplay, and other details regarding the placement of the creative.Projected events that have affected the board selection, such asupcoming sport events, can be provided to the advertiser to explain theboard selection. The information presented to the advertiser in thepreview of the campaign 106 will be a plan for execution of the campaignsince the actual campaign execution may be adjusted in response toreal-time data and events, which would make alternate placement ofcreative more favourable to the advertiser and result in different adplacement.

The advertiser is then given the choice to accept the campaign or goback and change one or more settings to obtain a different result. Theadvertiser 108 can be presented with suggestions on how to tweak thecampaign to obtain better results, such as targeting more specificdemographics or choosing different times of the day. Once the advertiseris satisfied with the campaign 108, the campaign is created 110. Oncethe conditions are met, such as respecting the start date of thecampaign, the ads will begin playing.

Campaign execution 112 occurs when ads start playing on boards that aredetermined to be relevant based on the criteria 104 b of the campaign.An ongoing report 114 can be maintained for the advertiser so that theadvertiser can adjust the campaign or adjust the creative or budget asdesired during the campaign. Some campaign criteria may also be tweakedto alter the board selection process. As the ads are played, the boardsreport back to the ad server, which aggregates the playback informationinto a report for the advertiser to consult at any time. Informationincludes which boards the ad has been played on, how often, at what timeof the day, which demographics were reached, which real-time eventscaused these particular boards to be selected, etc. This serves as proofof performance for the advertiser and provides statistics andexplanations for why and how the campaign took place the way it did.

At any point during the campaign, the advertiser may decide to extendthe campaign 116 by allocating more budget to the campaign. If thecampaign's budget is increased 118, the campaign can be extended induration or the scope broadened, targeting a wider variety of boardsand/or for a longer duration. The campaign ends 120 when the budget isfully exhausted and the advertiser has decided not to renew it byallocating more funds. The ads for this campaign will be removed fromthe loop and will not play anymore. Similar to the ongoing report 114, afinal report 122 is generated for the advertiser to review. Since thecampaign is over, the final report will not change over time. A detailedinformational analysis can be provided in the final report 122 toprovide the advertiser with the schedule and play of ads, and can alsoinclude the number and type of impressions, depending on the board typeand system enablement in the vicinity of the board.

Data Analysis

Data is analyzed for two main purposes: linking product information totarget demographics, and determining, for each board, what are thedemographics it is able to fulfill at different points in time. Thisanalysis can be carried out using a variety of data and Big Datasources, such as, for example:

Social medias trends

Aggregated Consumer insight

Real-time Video feeds

Activity-generated data

Location-based services

Traffic patterns and travel studies

Product information can also be linked to target demographics.Advertisers can explicitly decide which demographics they are targeting,or they can simply provide the system with information about what theyare advertising, such as the type of product being advertised. Sinceboards expose which demographics they are able to serve, rather thanwhich product type they are able to sell effectively, a link can beestablished between the information provided by the advertiser and whichdemographics should be targeted. Big Data trends are analyzedperiodically to populate a database allowing the system to link thesetogether.

In one example, an advertiser sells home insurance, but does not knowwhich demographic they should target for a particular ad campaign forearthquake insurance in particular. The system discovered, using BigData analysis techniques, that males between 30 and 45 years old livingin areas with a high risk of earthquake are the most common seekers ofhome insurance and could be a profitable demographic to target. Thisstep can be bypassed if the advertiser decides to target specificdemographics instead of letting the system determine them.

Board Selection

Board selection is the process used to maximize the global reach of theentire network. The goal is to optimize the distribution of creativeacross the network so that each board is used to its maximum potential.Demographics density is used to give each board a score. Denser boardsare those that are highly-specialized in reaching a specificdemographics, while less-dense boards are those that have a broad,general audience reach. As much as possible, creative is assigned todense boards that meet the advertising criteria, maximizing the boards'potential value while leaving the flexible boards open.

Each board has its own feature data, such as location, size andcapability. In order to receive appropriate content, each board iscategorized for certain demographics. Big Data trends can be analyzedand linked with the board vendor's inventory to discover whatdemographics can be served by each board. Such information is stored inthe Ad Server's database to be referenced when a Video Player needscontent to play. Special keywords can also be associated to a board,either manually or automatically through data analysis, helping to finetune selection of content to be played. This data is constantly beingadjusted in real-time based on trends coming in from various data andBig Data sources, such as social media, consumer insight, traffic,locational and news sources. Short-term projections can also be done,allowing content to be sent ahead of time to the boards so it is readyto play when needed. Some non-limiting examples of real-time Big Datasources are:

-   -   Social media activity (i.e. Facebook® posts, Twitter® feeds,        FourSquare® checkins, etc.)    -   Video feeds    -   Activity-generated data    -   Cellular data insights

One example of board classification is described for a board locatednear a shopping mall in New York. First, static data is analyzed tobroadly categorize the board. Using traffic studies provided by theTraffic Audit Bureau (TAB), which provides ratings for the Out of Homeindustry, the system knows that this particular board is seen by severaldemographics, the main two being “Male 25-40 Caucasian”, whichconstitutes 40% of the total audience, and “Female 25-40 Caucasian”,which constitutes 30% of the total audience. Other groups togetherconstitute the remaining 30%. It is known from urban planning data thatthis area contains a shopping mall as well as several office buildings,allowing the system to add “shoppers” and “office workers” to the listof demographic audience served by this board. Consumer spending trendsindicate that people in this area are mostly purchasing furniture,groceries and books, which can be added to the list of interests servedby this board.

Next, projected data is used to refine this categorization at certainpoints in time. By connecting with the data sources of major eventorganizers, the system knows that there is an upcoming concert for apopular boy band. This event is taking place on February 21^(st) at 7 PMwith a duration of 3 hours. The band's name and concert details arecross-referenced with various sources for music-related information andit is discovered that this band is mostly followed by 18-24 year oldfemales. Since the board is right in front of the concert's venue, it isexpected that it will have great visibility for this demographic aroundthe time the concert begins and ends. Thus, around 6:30 PM and 10:30 PM,the board should show ads relevant for 18-24 females. Nearby boardslocated close to public transit systems can also be re-categorized tofit this newly discovered demographic because this age group has beenshown, due to transit studies, to make heavy use of publictransportation systems. Thus, for two hours before and after theconcert, those boards would show ads relevant to this demographicbecause chances are they will represent a high percentage of the publictransit users during those times.

Finally, real-time trends are used to fine-tune the categorization on areal-time basis. This is demonstrated by the following example. On acalm Sunday afternoon, a group of protesters suddenly takes over thearea near a digital board. They are protesting for lower education fees.The event is highly publicized in social media both by protesters and bypeople passing by. The messages being posted on social networks aremostly photos of the protesters, which can be analyzed using facialrecognition to determine that most protesters are 18-24 males. The topicof conversation, protesting for lower education fees, gives a goodindication that the people protesting, and thus the people near theboard, are in or about to enter college. The profiles of people postingabout the protest can also be used to obtain limited information aboutwho is around the board, confirming that the board should display adsrelevant for 18-24 males that are college students.

FIG. 3 illustrates the process of categorizing digital boards. Thisallows the system to determine which boards can be used to mostefficiently display ads based on the criteria of the campaign. The boardoperator's inventory 202 constitutes a listing of the digital boardsowned by that operator as well as basic operational data such as theboard's identifier, location, size and format, functional capabilities,operating times, etc. Static data 204 is obtained from databases toprovide information such as, for example, traffic and populationsurveys, market studies, consumer spending trends. Classification of theboard 206 is based on static data as well as the board operator'sinventory 202 and the characteristics of each board in the inventory.This process combines the operator's inventory with the static data toaugment the inventory with information concerning the campaign criteriathat each board is able to fulfill. For each board, details such as itslocation are used to correlate it with pieces of information containedin the static data. For example, a board's geographic coordinates can beused to search the static data and locate which buildings are close toit, such as shopping malls, office buildings or schools. Suchinformation allows the board to be classified and tagged withdemographics and other campaign criteria it can serve. Static data willoccasionally be updated with more recent information from new surveysand studies when new static data is received 208. When that occurs, thenew data is imported into the system and the process at 206 is performedagain. The result of board classification step 206, is a set of boardswith baseline classification 210. This is a listing of the digitalboards owned by the operator augmented with categorization information,which serves as a baseline onto which other layers of data will beadded.

Projected data 212 is obtained for the area in the vicinity of the boardto determine if there is a unique event that could change thedemographic around the board compared to the usual demographic predictedby the static data 204. An update classification based on the projecteddata 214 is done. Similar to process 206, information identifying theboards is used to locate useful information concerning it in theprojected data. This will result in the board's classification beingaltered for certain periods of time in the future, such as serveddemographics being different when a nearby concert hall is hosting ashow. Projected data is monitored and updated 216 whenever a relevantchange is detected, the process at 214 is performed again for the boardsthat can be affected by the change in the data. The result of the boardclassification based on projected data 214 is a listing of the digitalboards owned by the operator augmented with categorization informationthat has a time component, varying at different moments in time based onwhat events are expected to happen nearby. This results in a set ofboards with baseline classification and future projections 218 based onstatic data as well as projected data.

Real-time data 220 is then obtained and factored into the classificationon a continual and ongoing basis. As previously described, real-timedata is obtained from social trending data such as traffic patterns,human movement patterns, and social media sources. The real-time data220 provides the final tweaks to the board's classification, making itbetter reflect the reality of what is happening right now in the nearbyarea. Real-time data 220, true to its name, its constantly changing andmust be monitored in real-time. Any piece of data that could be relevantdue to its message, author, location or other factor is analyzed anduseful information is added to the knowledge bank of real-time data 220so that a classification based on real-time data 222 can make use of itthe next time it is performed. The real-time data is continuously beingupdated 224 to provide a real-time analysis of board optimization.Real-time data can also be weighted based on the strength of theobserved trend either locally in the vicinity of the board, or globallyin the social media. In addition, real-time trends can cause theimmediate categorization of the board to temporarily shift to capture ademographic moving in the vicinity of the board. The analysis provides afully classified set of boards 226 which have been classified based on aset of static, projected and real-time data. The result of this processis a listing of the digital boards owned by the operator augmented withcategorization information that has a time component.

Projected and real-time data can therefore be used to temporarily alterwhat a board displays by identifying trends and tweaking the board'sselection of advertisements based on temporary demographics. If there isno noticeable projected data or real-time trending near the board,static data can provide adequate selection criteria to fall back on foradvertisement or creative selection.

FIG. 4 illustrates the process of selecting boards and dispatchingcampaigns to appropriate boards that should play their associatedcreative. This classification is what is used by the board selectionprocess described in FIG. 3. Information concerning the active campaigns302 created as part of campaign creation process explained in FIG. 2 isconsidered. Fully classified digital boards 304 augmented withcategorization information and real-time trends are the result of theprocess described in FIG. 3. A real-time board selection andoptimization process 306 then examines every active campaign and everyboard to determine which campaigns should play on which boards. Asdescribed, board and creative rotation selection is an ongoing processthat is continually updated in order to adjust the board selection basedon new information, such as updates to the boards classification causedby emerging real-time data trends. The listing of boards along withwhich campaign they should be playing at any exact moment is continuallyreconsidered based on real-time data and is regularly updated inreal-time to best reflect the current reality of the location of theboards.

As shown in FIG. 4, an ad serving processor 310 or digital feed providercomprising a processor comprises a communication system such as acomputer network, which is used to provide each board with informationon which campaign it should be playing as well as the selected creative.This processor also provides boards with updates should they need tochange the campaigns they are displaying. Digital billboards 312 a, 312b, 312 c, may be implemented through a combination of screens, computersand digital player software that receives directives from the ad serverand play content to satisfy the requirements of an advertising campaign.

As they play content, the digital billboards 312 a, 312 b, 312 c keeptrack of various playback-related statistics such as what creative theyplayed, how many times, at what time of the day, etc. Such ad playbackstatistics 314 are reported back to the ad server. The statistics forevery board are collected and aggregated into an update of campaigndetails 316, which can include the budget and report of where and whenthe campaign played. These statistics are used to fill reports andconsume the campaign's budget. The update of campaign details 316 can beprovided as a report to the advertiser to provide the advertiser withproof of performance and details about the campaign execution. This isfurther described in FIG. 2, steps 114 and 122.

The details of the board selection algorithm can be determined in avariety of ways. The challenge of board selection can be describedalgorithmically as follows: given boards with x associated features(some static, some projected and some dynamic/real-time) and y targetcategories (such as demographic, geographical location or targetindustry advertisement) each board can be classified in real time. Thealgorithm for board selection considers the available boards with theirassociated feature data as well as the target categories for thecampaign, and provides a matching between boards and campaigns thatmaximizes target delivery over one or more campaigns.

One approach that can be used to achieve this result is to use machinelearning methods for classification. Given features X(x1, . . . , xn)(discrete or real such as area or weekly impressions per demographiccategory) and possible target categories Y(y1, . . . , yn) derivehypothesis h(x) such that h: X→Y; given a board x with features x1, . .. , xn classify the board to one or more of y1, . . . , yn targetcategories. Other implementations for this algorithm include but are notlimited to support vector machine learning (SVM), neural nets forclassification, decision trees and random forests. In one illustrativeexample using the decision trees method:

-   -   Assume 4 target categories; LA 18-50, LA 18-24, NYC 18-50, NYC        18-24    -   Assume 2 panel features are available; area and weekly        impressions by demographic

Shown in FIG. 5 is a partial diagram of a decision tree classification,being the derived result from the available data set. In this example,board location is considered based on the geographical location of theboard and the product being advertised. For example, a soft drink withimages of people on a beach may sell well in Los Angeles (LA) inFebruary, but not in New York City (NYC). If the advertiser has ayounger demographic as the target for the creative, they may wish toselect a narrower target audience by age range, such as 18-24 year olds.Although the campaign may receive fewer overall impressions, eachimpression will have a greater impact from an advertising perspectivesince it is received by more members of the target audience.

After board classification is achieved, campaign targets are reviewedand boards are matched to campaigns according to the dynamicclassification. Since a board may have more than one viable targetcategory ranked, further optimization can be done to select boards basedon budget, running time, or other criteria. In some analyses, thepresent approach can include a continuous process of improvement as thealgorithm starts with a derived initial data set (i.e training set) andimproves it as more data is collected with time. In addition, as thenumber of available data sources grow, expectation maximization methodscan be used to fill in feature gaps within the data (i.e some data isavailable for certain boards but not for others). Also, the results canbe improved over time using feature selection methods.

Increasing the granularity of data extracted to pull out groups withparticular interests can also contribute to board selection by isolatingmovement patterns and correlating with projected data. In one example,visitors to an interior design show planned for a particular venue maybe of varying demographics, ages and sexes. However, the same populationhave a shared interest in paint colours and wall finishings. A creativefor a hardware store may include various individual advertisementsdirected at different types of customers, and the advertisementsdirected at customers with an interest in home décor, rather than thosewith an interest in plumbing, can be selected by type of advertisementas well as board selection near the interior design show venue.

Real-Time Price Adjustment

Since many advertisers can choose similar settings for their campaigns,bottlenecks may be created. Some boards could be highly targeted due tofulfilling popular demographics, while other boards may be underused.The system can therefore be adapted to adjust prices based on demand.Digital boards generally have a base rate card, which the board vendordetermines when adding the board to the system. Then, depending ondemand, the system can determine a ratio by which the base rate will bemultiplied, either increasing or decreasing cost for that board. Sinceadvertising systems usually select advertisers through a bidding system,the highest bidders will get the most popular and effective boards,while other advertisers will get cheaper, less effective boards, butwill pay less for them.

Such financial information can be presented to the advertiser atcampaign creation for consideration. When entering their campaigncriteria, advertisers can be presented with a plan of the boards theycould get and how many impressions they can expect to get for theirbudget, based on estimated trends for the moment their campaign willtake place. Alternatives are offered, allowing the advertiser tofine-tune their criteria to create a campaign that reaches their desiredaudience while respecting their budget. In one scenario, the advertisermay opt for a more limited campaign on the most expensive boards for ashorter period of time. Alternatively, a longer campaign on smaller orless prominent boards may be more favorable to the advertiser.

In another example, a board is located by an office building that isnear a sports arena. During the day, ads targeting office workers areplaying. Early in the evening, locational data as well as socialnetworks such as Twitter feeds or Facebook posts indicate that a lot ofyoung male football fans will be headed to the stadium to watch afootball game. The Video Player receives new content from the ad serverthat specifically targets young male football fans, such as televisionnetworks offering sports network packages. If instead the event at thesports arena is a figure skating competition, locational data as well associal networks will indicate an influx of women in the 25-50demographic to the area around the arena, and the Ad server will adjustthe content on the board to specifically target the local demographic,maximizing the number of appropriate viewers of the ad.

The system can thereby proactively plug into data sources of thosevenues, see what shows are coming up and be ready for it. If there is ashow for a famous rock band at Madison Square Garden on February 21stfrom 8 PM to 12 AM, the system can display ads appropriate for fans ofthe famous rock band between around 6:00 PM and 2:00 AM as people comein and out of the show. Social media data may also suggest that fans ofthe famous rock band may also be interested in various other bands, andadvertisements for concerts and music from these other bands can beshown on the same boards to the fans of the famous rock band.Correlation of the interest preferences of these fans with fan interestin other bands can be obtained from a multitude of social media and/ormusic sharing or music playing websites which have algorithms forselecting like music. Additionally, through social media interestmapping, it may be found that, for example, fans of the famous rock bandalso report ranking fantasy movies as their favorite movie genre. Inthis way, boards in the vicinity of the concert of the famous rock bandaround the same time as the concert can be used to effectively advertiseother rock bands with a similar sound to the famous rock band and/or newfantasy movies to a highly receptive audience.

All publications, patents and patent applications mentioned in thisSpecification are indicative of the level of skill of those skilled inthe art to which this invention pertains and are herein incorporated byreference to the same extent as if each individual publication, patent,or patent application was specifically and individually indicated to beincorporated by reference.

The invention being thus described, it will be obvious that the same maybe varied in many ways. Such variations are not to be regarded as adeparture from the spirit and scope of the invention, and all suchmodifications as would be obvious to one skilled in the art are intendedto be included within the scope of the following claims.

What is claimed is:
 1. A method for an out of home advertising campaign,the method comprising: supplying creative for the campaign; determiningcriteria for the campaign, the criteria comprising targeting ademographic; selecting one or more boards for display of the creative,the selecting based on static data, projected data, and optionallyreal-time data; and displaying the creative on the one or more boards.2. The method of claim 1, wherein the selecting is based on real-timedata, and wherein the real-time data is continuously updated.
 3. Themethod of claim 1, wherein the selecting of the one or more boards isupdated in real-time such that the selection of which boards andcreative is displayed are changed during the campaign.
 4. The method ofclaim 1, wherein the creative comprises images, video, web pages,dynamic elements, countdown timers, stock tickers, and any combinationthereof.
 5. The method of claim 1, further comprising generating anongoing report so that an advertiser can adjust the campaign, thecreative, and/or a budget during the campaign.
 6. An out of home digitalad server comprising: at least one digital board; a digital feedprovider comprising a processor for providing each board with creativeto be displayed; a computer processor for analysing static data,projected data and optionally real-time data to optimize board selectionbased on a demographic and determine which campaign each board should beplaying, the computer processor selecting which creative is displayed onthe at least one digital board; and a communication network to directcreative from the ad serving processor to the at least one digital boardbased on the selection of creative by the computer processor.
 7. The adserver of claim 6, further comprising a communication link to a datanetwork to obtain real-time data from the data network.
 8. The ad serverof claim 6, further comprising a plurality of digital boards.
 9. The adserver of claim 6, wherein the at least one digital board furthercomprises a memory for recording playback-related statistics on thecreative played on the at least one digital board.
 10. The ad server ofclaim 6, wherein the at least one digital board further comprises amemory for storing the creative content, which is then downloaded andstored locally in a local database or file system.
 11. A method forexecuting an out of home advertising campaign, the method comprising:determining static demographic data for at least one digital board;determining projected demographic data for the at least one digitalboard; determining real-time demographic data for the at least onedigital board combining the static demographic data, projecteddemographic data, and real-time demographic data to select and displayadvertisements to be displayed on the at least one board.
 12. The methodof claim 11, wherein the real-time data based on social media trendingor human movement tracking.